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Paper Abstract and Keywords
Presentation 2026-08-06 09:55
A Study on Cluster-Based Task-Adaptive Selection of Large Language Models
Takeshi Tsuchiya (Chuo Univ.), Ryuichi Mochizuki (MR) IN2026-17
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a method for selecting a large language model (LLM) whose performance is well suited to a given user query by adapting to the characteristics of that query. Prior investigations have revealed that the performance of LLM outputs is difficult to extract and visualize at the level of individual queries. To address this, the proposed method constructs performance clusters that organize and manage the output performance of each LLM according to input task type. When a user submits a query, the most suitable LLM can be selected based on the similarity between the query and each performance cluster. In this paper, we conduct an evaluation under conditions simulating a practical deployment environment, demonstrating that the proposed method achieves response quality comparable to that of similar methods, while also dynamically and evenly allocating queries among multiple distributed LLMs. Based on these results, we discuss the effectiveness and applicability of the proposed method in real-world environments.
Keyword (in Japanese) (See Japanese page) 
(in English) LLM Routing / LLM Evaluation / Output Features of LLMs / Input task classification / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 144, IN2026-17, pp. 7-12, Aug. 2026.
Paper # IN2026-17 
Date of Issue 2026-07-30 (IN) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IN2026-17

Conference Information
Committee IN CCS  
Conference Date 2026-08-06 - 2026-08-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Sapporo Educaton and Culture Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network Science, Future Network, Cloud/SDN/Virtualization, Contents Delivery/Contents Exchange, and others 
Paper Information
Registration To IN 
Conference Code 2026-08-IN-CCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Cluster-Based Task-Adaptive Selection of Large Language Models 
Sub Title (in English)  
Keyword(1) LLM Routing  
Keyword(2) LLM Evaluation  
Keyword(3) Output Features of LLMs  
Keyword(4) Input task classification  
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1st Author's Name Takeshi Tsuchiya  
1st Author's Affiliation Chuo University (Chuo Univ.)
2nd Author's Name Ryuichi Mochizuki  
2nd Author's Affiliation MR System (MR)
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Speaker Author-1 
Date Time 2026-08-06 09:55:00 
Presentation Time 25 minutes 
Registration for IN 
Paper # IN2026-17 
Volume (vol) vol.126 
Number (no) no.144 
Page pp.7-12 
#Pages 6 
Date of Issue 2026-07-30 (IN) 

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